jacobzweig

# Build model
in_id = tf.keras.layers.Input(shape=(max_seq_length,), name="input_ids")
in_mask = tf.keras.layers.Input(shape=(max_seq_length,), name="input_masks")
in_segment = tf.keras.layers.Input(shape=(max_seq_length,), name="segment_ids")
bert_inputs = [in_id, in_mask, in_segment]

# Instantiate the custom Bert Layer defined above
bert_output = BertLayer(n_fine_tune_layers=10)(bert_inputs)

# Build the rest of the classifier 

jacobzweig

class BertLayer(tf.layers.Layer):
    def __init__(self, n_fine_tune_layers=10, **kwargs):
        self.n_fine_tune_layers = n_fine_tune_layers
        self.trainable = True
        self.output_size = 768
        super(BertLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        self.bert = hub.Module(
            bert_path,

jacobzweig

# Instantiate tokenizer
tokenizer = create_tokenizer_from_hub_module()

# Convert data to InputExample format
train_examples = convert_text_to_examples(train_text, train_label)
test_examples = convert_text_to_examples(test_text, test_label)

# Convert to features
(train_input_ids, train_input_masks, train_segment_ids, train_labels 
) = convert_examples_to_features(tokenizer, train_examples, max_seq_length=max_seq_length)

jacobzweig

# Load all files from a directory in a DataFrame.
def load_directory_data(directory):
  data = {}
  data["sentence"] = []
  data["sentiment"] = []
  for file_path in os.listdir(directory):
    with tf.gfile.GFile(os.path.join(directory, file_path), "r") as f:
      data["sentence"].append(f.read())
      data["sentiment"].append(re.match("\d+_(\d+)\.txt", file_path).group(1))
  return pd.DataFrame.from_dict(data)

jacobzweig

# -*- coding: utf-8 -*-
"""
Created on Sun Sep 24 06:22:04 2017

@author: jacobzweig
"""
import random

class Prisoner:
    def __init__(self, id, numPrisoners):